A deep neural network-based hybrid recommender system with user-user networks

In today's digital age, choosing the right product, web page, news article, or even a research paper like this one from an extensive number of options is one of the most tedious tasks. The resolution to this problem is using a recommender system(RS), which helps you choose the suitable item according to your profile. In this research, We present a novel deep neural network based hybrid recommender system that addresses the lacunas of traditional Collaborative Filtering (CF) and current hybrid systems while also delivering higher accuracy in recommendations. Due to insufficient training data, CF recommender systems suffer from low accuracy, linear latent factor, and cold-start problem. To overcome these problems, we employ a Deep neural network-based approach which uses user and item vectors to encapsulate users' and items' data to train on High dimensionality non-linear data to provide more accurate recommendations. User-user networks are employed to provide a better collaboration and synergy facet to our model. In our approach, Combining user-user networks with Deep neural networks yields higher predictive accuracy and better running time than other state-of-art methods. Extensive experimentation on publicly available Flixster and MovieLens Datasets concludes that our technique outperforms current premier methods by achieving improvement of 19% in RMSE, 9.2% in MAE and 4.1% in F1 Score.